In [1]:
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
plt.rcParams["figure.figsize"] = (20,10)
sns.set()
In [2]:
df = pd.read_csv("NZDep 1991-2013 CAU with TALB2018.csv", na_values=0)
In [3]:
df.columns
Out[3]:
Index(['AU2013_V1_', 'AU2013_V_1', 'AREA_SQ_KM', 'LAND_AREA_', 'Shape_Leng',
       'NZDep1991 CAU score', 'NZDep1991 CAU scale', 'NZDep1991 pop',
       'NZDep1996 CAU name', 'NZDep1996 CAU score', 'NZDep1996 CAU scale',
       'NZDep1996 CAU pop', 'NZDep2001 CAU name', 'NZDep2001 CAU scale',
       'NZDep2001 CAU score', 'NZDep2006 CAU name', 'NZDep2006 CAU scale',
       'NZDep2006 CAU score', 'NZDep2013 CAU name', 'NZDep2013 CAU scale',
       'NZDep2013 CAU score', 'TALB2018_V', 'TALB2018_1'],
      dtype='object')
In [4]:
years = ["1991", "1996", "2001", "2006", "2013"]
scores = df[[f"NZDep{year} CAU score" for year in years]]
scales = df[[f"NZDep{year} CAU scale" for year in years]]
display(scores.describe())
display(scales.describe())
NZDep1991 CAU score NZDep1996 CAU score NZDep2001 CAU score NZDep2006 CAU score NZDep2013 CAU score
count 1468.000000 1531.000000 1624.000000 1734.000000 1867.000000
mean 998.558583 998.706074 997.485837 996.632065 995.086235
std 77.103141 80.304591 81.279356 79.744490 80.129521
min 812.000000 856.000000 858.000000 864.000000 850.000000
25% 940.000000 940.000000 936.000000 936.000000 935.000000
50% 987.000000 982.000000 980.000000 977.000000 975.000000
75% 1046.000000 1041.000000 1041.250000 1039.000000 1038.000000
max 1313.000000 1377.000000 1322.000000 1452.000000 1356.000000
NZDep1991 CAU scale NZDep1996 CAU scale NZDep2001 CAU scale NZDep2006 CAU scale NZDep2013 CAU scale
count 1468.000000 1531.000000 1624.000000 1734.000000 1867.000000
mean 5.588556 5.578707 5.573276 5.554787 5.500268
std 2.878533 2.875780 2.869226 2.869371 2.871558
min 1.000000 1.000000 1.000000 1.000000 1.000000
25% 3.000000 3.000000 3.000000 3.000000 3.000000
50% 6.000000 6.000000 6.000000 6.000000 6.000000
75% 8.000000 8.000000 8.000000 8.000000 8.000000
max 10.000000 10.000000 10.000000 10.000000 10.000000
In [5]:
scores.plot.hist(bins=100, alpha=0.5)
plt.xlabel('NZDep score')
plt.show()
In [6]:
for i, row in df.iterrows():
    plt.plot(years, [row.get(f"NZDep{year} CAU score") for year in years])
plt.xlabel("year")
plt.ylabel("NZDep score")
plt.show()
In [7]:
scores_by_TALB = df.groupby(by="TALB2018_1").mean()[[f"NZDep{year} CAU score" for year in years]]
scores_by_TALB
Out[7]:
NZDep1991 CAU score NZDep1996 CAU score NZDep2001 CAU score NZDep2006 CAU score NZDep2013 CAU score
TALB2018_1
Albert-Eden Local Board Area 1007.695652 986.125000 984.708333 982.083333 969.958333
Area Outside Territorial Authority NaN NaN NaN NaN NaN
Ashburton District 973.090909 963.454545 953.545455 964.727273 959.625000
Buller District 1070.200000 1075.666667 1051.733333 1037.533333 1011.133333
Carterton District 966.250000 959.000000 942.750000 943.250000 946.250000
... ... ... ... ... ...
Westland District 973.714286 996.857143 973.000000 985.789474 967.789474
Whakatane District 1056.777778 1082.315789 1087.210526 1083.421053 1070.052632
Whanganui District 1036.620690 1043.034483 1043.068966 1047.275862 1057.068966
Whangarei District 1021.000000 1019.916667 1028.918919 1014.372093 1031.219512
Whau Local Board Area 1010.307692 1005.230769 1018.153846 1017.000000 1018.312500

88 rows × 5 columns

In [8]:
fig = go.Figure()
for i, row in scores_by_TALB.iterrows():
    fig.add_trace(go.Line(x=years, y=[row.get(f"NZDep{year} CAU score") for year in years], name=i))
fig.update_layout(
    title="NZDep over time",
    xaxis_title="year",
    yaxis_title="NZDep score",
    showlegend=False
)

fig.show()

/usr/local/lib/python3.6/dist-packages/plotly/graph_objs/_deprecations.py:385: DeprecationWarning:

plotly.graph_objs.Line is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Line
  - plotly.graph_objs.layout.shape.Line
  - etc.
In [11]:
TALBs = list(df.TALB2018_1.value_counts().keys())
fig = make_subplots(
    rows=len(TALBs), cols=1,
    subplot_titles=TALBs)
for i, TALB in enumerate(TALBs):
    #print(TALB)
    data_for_this_TALB = df[df.TALB2018_1 == TALB][[f"NZDep{year} CAU scale" for year in years]]
    for decile in range(1,11):
        pct = (data_for_this_TALB == decile).sum() / (~pd.isna(data_for_this_TALB)).sum() * 100
        fig.add_trace(go.Bar(x=years, y=pct, name=decile), row=i+1, col=1)
fig.update_layout(barmode='stack', title="NZDep", xaxis_title="year", yaxis_title="%", legend_title="decile", height=len(TALBs) * 200)
fig.show()
In [ ]: